Purpose -The purpose of this paper is to review the literature on maintenance optimization models and associated case studies. For these optimization models critical observations are made. Design/methodology/approach -The paper systematically classifies the published literature using different techniques, and also identifies the possible gaps. Findings -The paper outlines important techniques used in various maintenance optimization models including the analytical hierarchy process, the Bayesian approach, the Galbraith information processing model and genetic algorithms. There is an emerging trend towards uses of simulation for maintenance optimization which has changed the maintenance view. Practical implications -A limited literature is available on the classification of maintenance optimization models and on its associated case studies. The paper classifies the literature on maintenance optimization models on different optimization techniques and based on emerging trends it outlines the directions for future research in the area of maintenance optimization. Originality/value -The paper provides many references and case studies on maintenance optimization models and techniques. It gives useful references for maintenance management professionals and researchers working on maintenance optimization.

ObjectiveHeart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome. We aimed to derive HFpEF phenotype-based groups ('phenogroups') based on clinical and echocardiogram data using machine learning, and to compare clinical characteristics, proteomics and outcomes across the phenogroups.MethodsWe applied model-based clustering to 32 echocardiogram and 11 clinical and laboratory variables collected in stable condition from 320 HFpEF outpatients in the Karolinska-Rennes cohort study (56% female, median 78 years (IQR: 71–83)). Baseline proteomics and the composite end point of all-cause mortality or heart failure (HF) hospitalisation were used in secondary analyses.ResultsWe identified six phenogroups, for which significant differences in the prevalence of concomitant atrial fibrillation (AF), anaemia and kidney disease were observed (p<0.05). Fifteen out of 86 plasma proteins differed between phenogroups (false discovery rate, FDR<0.05), including biomarkers of HF, AF and kidney function. The composite end point was significantly different between phenogroups (log-rank p<0.001), at short-term (100 days), mid-term (18 months) and longer-term follow-up (1000 days). Phenogroup 2 was older, with poorer diastolic and right ventricular function and higher burden of risk factors as AF (85%), hypertension (83%) and chronic obstructive pulmonary disease (30%). In this group a third experienced the primary outcome to 100 days, and two-thirds to 18 months (HR (95% CI) versus phenogroups 1, 3, 4, 5, 6: 1.5 (0.8–2.9); 5.7 (2.6–12.8); 2.9 (1.5–5.6); 2.7 (1.6–4.6); 2.1 (1.2–3.9)).ConclusionsUsing machine learning we identified distinct HFpEF phenogroups with differential characteristics and outcomes, as well as differential levels of inflammatory and cardiovascular proteins.

The targets for saccadic eye movements in natural visual scenes are spatially extended objects, yet saccades land at a single position within them. To characterize the spatial transformation that determines the saccadic goal position within attended objects, we studied saccadic localization of large patterns of random dots. Saccades landed with a high degree of precision near the center-of-gravity of the patterns (average error < 10%; SDs around the center-of-gravity = 7-11% of target eccentricity). Predictions of landing position were improved by using a weighted center-of-gravity, in which the weight assigned to each dot was reduced by the presence of neighboring dots. Weighting based either on the eccentricity of dots or their position relative to the boundary of the pattern had no effect. The results can be accounted for by a spatial transformation in which the "local signs" of an initial array of detectors, weighted by the activity of each, are averaged to yield the saccadic goal. This model can account for accurate and precise saccadic localization of large targets, while preserving sensitivity to local pattern characteristics. Unlike models of recognition, the boundary of the object has the same status as the internal details.

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Visible-light-assisted photocatalysis for the degradation of organic pollutants has recently become an efficient green approach in the field of environmental pollution abatement. Herein, graphene-templated zeolite-imidazolate framework (ZIF-67) derived, Co nanoparticle embedded, nitrogen-doped carbon nanotubes (G-Co-NCNTs) have been developed as a promising, inexpensive, high-yield photocatalyst to decompose reactive black 5 (RB5) under visible light irradiation. Morphology and structural characterization studies revealed that the growth of NCNTs along with pyridinic N content and the abundance of meso-micropores were greater in G-Co-NCNT than in Co-NCNT itself, suggesting the importance of graphene for in situ growth of ZIF-67 on GO. DRS study reveals that G-Co-NCNT exhibited low optical band gap (∼2.9 eV), assisting in the promotion of photoresponse behavior. The photocatalytic activity of our designed G-Co-NCNT hybrid showed excellent dye degradation ability (98%) after 60 min with a wide range of pH tolerance and promising reusability even after five cycles (93%) under visible light, while Co-NCNT demonstrated only about 62% dye degradation, further implying the importance of graphene and oriented NCNTs for dye degradation. Therefore, the G-Co-NCNT hybrid could be used as an efficient photocatalyst for the remediation of organic pollutants in wastewater.

Synthesis of compounds that can prevent bacterial resistance is of huge interest and gaining immense popularity. Cobalt (Co) is one of the cheaper transition metals and its nano form has not been studied in details for antibacterial actions. Comparative analysis of Co nanoparticles with bulk Co and standard antibacterials are also lacking. In our study, concentration dependent action of Co nanoparticles was observed from 0.125 to 128.0 µg/ml against S. aureus and E. coli. Zone of inhibition of Co nanoparticles was better against E. coli than S. aureus. Co nanoparticles were markedly betterthan bulk Co, oxytetracycline and gentamicin. Activity index and fold increase of Co nanoparticles were higher at most of the concentrations. In conclusion, Co nanoparticles showed better antibacterial action than other tested compounds against S. aureus and E. coli particularly at lower concentrations, and their use may be extended in different biomedical fields in future.

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Blockchain Technology, the fundamental technology behind Bitcoin, has drawn considerable recognition ever since its origin. Its potential has gained significant interest in various applications, varying from the music industry, financial services, Internet-of-Things (IoT), smart grid, edge computing, cybersecurity, and the healthcare industry. However, the information is divided amongst several intermediaries involved with adverse impacts on data quality in the healthcare domain. In the near future, blockchain technology can reshape the way the healthcare industry works by providing personalized and reliable patient data management, reforming the traditional healthcare practices, secure mechanisms for data sharing, efficient pharmaceutical supply chain management, and drug traceability and many more. In this study, an extensive literature review has been provided that includes the different prospects of using blockchain technology in healthcare. The review investigates the work done to enable the amalgamation of IoT and Blockchain in the health ecosystem. Significant blockchain-based healthcare use cases such as data storage, data sharing, drug traceability, clinical trials, and remote patient monitoring are investigated. Further, the Internet of Things and blockchain technology-based SWOT (Strength, Weakness, Opportunity, and Threat) analysis and the challenges linked in the healthcare domain because of the enactment of IoT and blockchain technology are discussed to support advanced studies in this domain.